Dearden John C
School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England.
J Comput Aided Mol Des. 2003 Feb-Apr;17(2-4):119-27. doi: 10.1023/a:1025361621494.
It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled.
为了尽可能减少因在后期开发甚至临床试验中发现毒性而导致昂贵的药物研发失败，尽早确定潜在的毒性问题至关重要。鉴于现在通过组合化学和高通量筛选处理的大量化合物库，甚至在合成之前就建议对推定的毒性进行鉴定。因此，需要使用预测毒理学。本文讨论了一些用于毒性预测的计算机模拟方法。定量构效关系（QSARs）主要与特定化学类别相关，长期以来一直用于此目的，并且存在针对广泛毒性终点的QSARs。然而，对于非常多样化的化合物库的毒性预测也存在QSARs，尽管通常此类QSARs属于分类类型；也就是说，它们只是预测一种化合物是否有毒，而不表明毒性水平。文中给出了所有这些方面的示例。有许多专家系统可用于毒性预测，其中大多数涵盖一系列毒性终点。所讨论的系统包括TOPKAT、CASE、DEREK、HazardExpert、OncoLogic和COMPACT。对这些系统预测致癌性能力的比较测试表明，仍需改进。建议采用共识方法，即将几个预测系统的结果汇总。
